Explore the critical balance between data utility and security in the realm of Large Language Models (LLMs) in this comprehensive conference talk from BSidesSF 2024. Delve into the pressing question of whether fine-tuning LLMs with sensitive data such as PII, PHI, and FII poses a risk of data leakage. Join speakers Rob Ragan and Aashiq Ramachandran as they examine the challenges and implications of customizing LLMs for specialized use cases, offering insights into the privacy paradox faced by individuals and companies in the era of advanced AI language models.
LLM Privacy Paradox: Balancing Data Utility with Security - BSidesSF 2024
Security BSides San Francisco via YouTube
Overview
Syllabus
BSidesSF 2024 - LLM Privacy Paradox: Balancing Data Utility... (Rob Ragan, Aashiq Ramachandran)
Taught by
Security BSides San Francisco
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